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Joint Denoising and Line Distortion Correction for Raster-Scanned Image Series

In: Multiscale, Nonlinear and Adaptive Approximation II

Author

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  • Benjamin Berkels

    (RWTH Aachen University, Aachen Institute for Advanced Study in Computational Engineering Science (AICES))

  • Peter Binev

    (University of South Carolina)

Abstract

The problem of noise in a general data acquisition procedure can be resolved more accurately if it is based on a model that describes well the distortions of the data including both spatial and intensity changes. The focus of this article is the modeling of the position distortions during sequential data acquisitions. A guiding example is the data obtained by Scanning Transmission Electron Microscopy (STEM) and High Angular Annular Dark Field (HAADF) data, in particular. The article discusses different models of the position noise and their numerical implementations comparing some computational results.

Suggested Citation

  • Benjamin Berkels & Peter Binev, 2024. "Joint Denoising and Line Distortion Correction for Raster-Scanned Image Series," Springer Books, in: Ronald DeVore & Angela Kunoth (ed.), Multiscale, Nonlinear and Adaptive Approximation II, pages 99-114, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-75802-7_6
    DOI: 10.1007/978-3-031-75802-7_6
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